Safety management theory and the military expeditionary organization: A critical theoretical reflection

Management of safety within organizations has become a key topic within safety science. Theorizing on this subject covers a diverse pallet of concepts such as “resilience” and “safety management systems”. Recent studies indicate that safety management theory has deficiencies. Our interpretation of these deficiencies is that much confusion originates from the issue that crucial meta-theoretical assumptions are mostly implicit or applied inconsistently. In particular, we argue that these meta-theoretical assumptions are of a systems theoretical nature. Therefore, we provide a framework that will be able to explicate and reflect on systems theoretical assumptions. With this framework, we analyze the ability of two frequently used safety management theories to tackle the problem of managing safety of Dutch military expeditionary organizations. This paper will show that inconsistent and implicit application of systems theoretical assumptions in these safety management theories results in problems to tackle such a practical problem adequately. We conclude with a reflection on the pros and cons of our framework. Also, we suggest particular meta-theoretical aspects that seem to be essential for applying safety management theory to organizations

Joint inversion scheme with an adaptive coupling strategy - applications on synthetic and real data sets

Joint inversion strategies for geophysical data have become increasingly popular since they allow to combine complementary information from different data sets in an efficient way. However, for joint inversion algorithms that use methods that are sensitive to different parameters it is important that they are not restricted to specific survey arrays and subsurface conditions. Hence, joint inversion schemes are needed that 1) adequately balance data from the different methods and 2) use links between the parameter models that are suited for a wide range of applications. Here, we combine MT, seismic tomography and gravity data in a non-linear joint inversion that accounts for these critical issues. Data from the different methods are inverted separately and are joined through constrains accounting for parameter relationships. An advantage of performing the inversions separately (and not together in one matrix) is that no relative weighting between the data sets is required. To avoid that the convergence behavior of the inversions is profoundly disturbed by the coupling, the strengths of the associated constraints are re-adjusted at each iteration. As criteria to control the adaption of the coupling strengths we used a general version of the well-known discrepancy principle. Adaption of the coupling strengths makes the joint inversion scheme also applicable to subsurface conditions, for which the assumed relationships are only a rough first order approximation. So, the coupling between the different parameter models is automatically reduced if for some structures the true rock property behaviors differ significantly from the assumed relationships (e.g. the atypical density-velocity behavior of salt). We have tested our scheme first on different synthetic 2-D models for which the assumed parameter relationships are everywhere valid. We observe that the adaption of the coupling strengths makes the convergence of the inversions very robust and that the final results are close to the true models. In a next step the scheme has been applied on models for which the assumed parameter relationships are invalid for some structures. For these structures deviations from the relationships are present in the final results; however, for the remaining structures the relative behaviors of the physical parameters are still approximately described by the assumed relationship. Finally, we applied our joint inversion scheme on seismic, MT and gravity data collected offshore the Faroe Islands, where basalt intrusions are present

Using Empirical Mode Decomposition (EMD) for the processing of marine MT data

Magnetotelluric (MT) method determines a frequency dependent impedance tensor using the spectra of associated time-varying horizontal electric and magnetic fields measured at the Earth’s surface. In this abstract, we present a dynamic time series analysis method dealing the non-stationary MT data to infer the impedance tensor. Most current methods to determine the spectra use Fourier transform based procedure and, therefore, assume that the signals are stationary over the record length. We introduce a new method for dealing with non-stationarity of the MT time series based upon empirical mode decomposition (EMD) method, a dynamic time series analysis method. Using EMD complicated data sets can be decomposed into a finite and small number of "intrinsic mode functions" (IMFs), which are mono-component signals and allow the calculation of physical meaningful instantaneous frequencies. EMD has no bias due to non-stationary of geomagnetic time series, since the IMFs are based entirely on signal characteristics and not on any given set of base functions such as sines and cosines in the Fourier transform or wavelets in the Wavelet transform. We use the EMD method to decompose MT data into IMFs and calculate the instantaneous frequencies and spectra to determine the impedance tensor. The method is tested in synthetic and real marine MT data sets, the obtained estimate results are reliable compared to frequently-used BIRRP processing method. Furthermore, new method has the possibility of noise visualization and filtering, which is especially important in marine applications, where noise free time segments maybe short

Towards 3D joint inversion of full tensor gravity, magnetotelluric and seismic refraction data

EGU2010-4184-2 Joint inversion of different datasets is emerging as an important tool to enhance resolution and decrease inversion artifacts in structurally complex areas. Performing the inversion in 3D allows us to investigate such complex structures but requires computationally efficient forward modeling and inversion methods. Furthermore we should be able to flexibly change inversion parameters, coupling approaches and forward modeling schemes in order to find a suitable approach for the given target. We present a 3D joint inversion framework for scalar and full tensor gravity, magnetotelluric and seismic data that allows us to investigate different approaches. It consists of two memory efficient gradient based optimization techniques, L-BFGS and NLCG, and optimized parallel forward solvers for the different datasets. In addition it provides the necessary flexibility in terms of model parametrization and coupling method by completely separating the inversion parameters and geometry from the parametrization of the individual method. This separation allows us to easily switch between completely different types of parameterizations and use structural coupling as well as coupling based on parameter relationships for the joint inversion. First tests on synthetic data with a fixed parameter relationship coupling show promising results and demonstrate that 3D joint inversion is becoming feasible for realistic size models

Adaption and GPU based parallelization of the code TEMDDD for the 3D modelling of CSEM data

The finite difference time domain code TEMDDD was modified for the 3D forward modeling of marine CSEM data. After changes in the code, which make it possible to create model geometries typically encountered in marine CSEM experiments, parts of the code have been parallelized using massive parallelization on graphic cards. Parts of the singular value decomposition, which is the most time consuming part of the code, have been successfully ported with massive speed-ups (8-12x faster) observed as compared to the standard code. The full parallelization of the code is still work in progress

Joint inversion of receiver functions, surface wave dispersion, and magnetotelluric data

We present joint inversion of magnetotelluric, receiver function, and Raleigh wave dispersion data for a one‐dimensional Earth using a multiobjective genetic algorithm (GA). The chosen GA produces not only a family of models that fit the data sets but also the trade‐off between fitting the different data sets. The analysis of this trade‐off gives insight into the compatibility between the seismic data sets and the magnetotelluric data and also the appropriate noise level to assume for the seismic data. This additional information helps to assess the validity of the joint model, and we demonstrate the use of our approach with synthetic data under realistic conditions. We apply our method to one site from the Slave Craton and one site from the Kaapvaal Craton. For the Slave Craton we obtain similar results to our previously published models from joint inversion of receiver functions and magnetotelluric data but with improved resolution and control on absolute velocities. We find a conductive layer at the bottom of the crust, just above the Moho; a low‐velocity, low‐resistivity zone in the lithospheric mantle, previously termed the Central Slave Mantle Conductor; and indications of the lithosphere‐asthenosphere boundary in terms of a decrease in seismic velocity and resistivity. For the Kaapvaal Craton both the seismic and the MT data are of lesser quality, which prevents as detailed and robust an interpretation; nevertheless, we find an indication of a low‐velocity low‐resistivity zone in the mantle lithosphere. These two examples demonstrate the potential of joint inversion, particularly in combination with nonlinear optimization methods

Joint inversion of long-period magnetotelluric data and surface-wave dispersion curves for anisotropic structure: Application to data from Central Germany

Geophysical datasets sensitive to different physical parameters can be used to improve resolution of Earth's internal structure. Herein, we jointly invert long-period magnetotelluric (MT) data and surface-wave dispersion curves. Our approach is based on a joint inversion using a genetic algorithm for a one-dimensional (1-D) isotropic structure, which we extend to 1-D anisotropic media. We apply our new anisotropic joint inversion to datasets from Central Germany demonstrating the capacity of our joint inversion algorithm to establish a 1-D anisotropic model that fits MT and seismic datasets simultaneously and providing new information regarding the deep structure in Central Germany. The lithosphere/asthenosphere boundary is found at approx. 84 km depth and two main anisotropic layers with coincident most conductive/seismic fast-axis direction are resolved at lower crustal and asthenospheric depths. We also quantify the amount of seismic and electrical anisotropy in the asthenosphere showing an emerging agreement between the two anisotropic coefficients

Deciphering the State of the Lower Crust and Upper Mantle With Multi-Physics Inversion

The composition of the lower crust is a key factor in understanding tectonic activity and deformation within the Earth. In particular, the presence or absence of melt or fluids has strong control on tectonic evolution. Multi-physics inversion results from the western United States indicate that tectonic inheritance plays a much stronger role in determining the location of melt in the lower crust than previously thought. Even in a currently active area such as the Yellowstone Hotspot, the results suggest that fluid dominated structures and fluid free regions are located directly next to each other. This is incompatible with the commonly used model of recent tectonic activity as a main controlling factor for the presence of fluids or melt. These results have global implications for how geophysical models are interpreted and how they can be related to geodynamic simulations